Path planning in the multi-robot system refers to calculating a set of actions for each robot, which will move each robot to its goal without conflicting with other robots. Lately, the research topic has received significant attention for its extensive applications, such as airport ground, drone swarms, and automatic warehouses. Despite these available research results, most of the existing investigations are concerned with the cases of robots with a fixed movement speed without considering uncertainty. Therefore, in this work, we study the problem of path-planning in the multi-robot automatic warehouse context, which considers the time-varying and uncertain robots' movement speed. Specifically, the path-planning module searches a path with as few conflicts as possible for a single agent by calculating traffic cost based on customarily distributed conflict probability and combining it with the classic A* algorithm. However, this probability-based method cannot eliminate all conflicts, and speed's uncertainty will constantly cause new conflicts. As a supplement, we propose the other two modules. The conflict detection and re-planning module chooses objects requiring re-planning paths from the agents involved in different types of conflicts periodically by our designed rules. Also, at each step, the scheduling module fills up the agent's preserved queue and decides who has a higher priority when the same element is assigned to two agents simultaneously. Finally, we compare the proposed algorithm with other algorithms from academia and industry, and the results show that the proposed method is validated as the best performance.

近年来，商业上可用和负担得起的四足动物机器人激增，其中许多平台在研究和行业中都被积极使用。随着腿部机器人的可用性的增长，对这些机器人能够执行有用技能的控制器的需求也是如此。但是，大多数用于控制器开发的基于学习的框架都集中在培训机器人特定的控制器上，该过程需要为每个新机器人重复。在这项工作中，我们引入了一个用于训练四足机器人的广义运动（Genloco）控制器的框架。我们的框架合成了可以部署在具有相似形态的各种四足动物的机器人上的通用运动控制器。我们提出了一种简单但有效的形态随机化方法，该方法在程序上生成了一组训练的模拟机器人。我们表明，通过对这套模拟机器人进行训练，我们的模型获得了更多的通用控制策略，这些策略可以直接转移到具有多种形态的新型模拟和真实世界机器人中，在训练过程中未观察到。

辐射脑病（REP）是鼻咽癌（NPC）放疗最常见的并发症。非常希望协助临床医生优化NPC放射疗法方案，以减少放射疗法诱导的颞叶损伤（RTLI），该疗程根据REP发作的可能性。据我们所知，这是通过在NPC放射治疗方案中共同利用图像和非图像数据来预测放疗诱导的REP的首次探索。我们将代表预测作为生存分析任务，并根据一致性指数（CI）评估预测准确性。我们设计了一个深层多模式生存网络（MSN），该网络（MSN）具有两个特征提取器，以从多模式数据中学习判别特征。一个功能提取器在非图像数据上施加特征选择，另一个功能提取器从图像中学习视觉特征。因为直接使CI最大化的CI（BCI）损耗函数对每批采样不均匀。因此，我们提出了一种新型的加权CI（WCI）损失函数，以通过双平均操作分配其不同的权重有效地利用所有REP样本。我们进一步引入了WCI温度高参数，以增强样本对的风险差异，以帮助建模收敛。我们在私人数据集上广泛评估WCI，以证明其对同行的可爱性。实验结果还表明，NPC放射疗法的多模式数据可以为REP风险预测带来更多收益。

在肺结节的管理中，我们希望根据其在计算机断层扫描（CT）扫描的直径变化方面预测结节的演变，然后根据结节不断增长的趋势的预测结果提供后续建议。为了提高肺结节增长趋势预测的性能，与连续CT扫描中相同结节的变化进行比较至关重要。在此激励的情况下，我们从国家肺筛查试验（NLST）数据集进行了两次以上的CT扫描，筛选了4,666名受试者，以组织一个名为NLSTT的颞数据集。在具体上，我们首先检测并配对感兴趣的区域（ROI），该区域涵盖了基于注册的CT扫描的相同结节。之后，我们通过模型预测结节的纹理类别和直径大小。最后，我们根据直径的变化来注释每个结节的演化类别。基于构建的NLSTT数据集，我们建议一个暹罗编码器同时利用从连续的CT扫描中检测到的3D ROI的判别特征。然后，我们在新小时设计一个时空混合器（STM）来利用连续3D ROI中同一结节的间隔变化，并捕获结节区域的空间依赖性和当前的3D ROI。根据临床诊断常规，我们采用层次损失来更多地关注生长的结节。我们有组织的数据集上的广泛实验证明了我们提出的方法的优势。我们还对内部数据集进行了实验，以通过将其与熟练的临床医生进行比较来评估我们方法的临床实用性。

在许多Web应用程序中，基于深度学习的CTR预测模型（简短的CTR模型）被广泛采用。传统的深入CTR模型以静态方式学习模式，即，在所有实例中，网络参数都是相同的。但是，这种方式几乎无法表征可能具有不同基础分布的每个实例。它实际上限制了深CTR模型的表示功能，从而导致了次优结果。在本文中，我们提出了一个高效，有效和通用的模块，称为自适应参数生成网络（APG），该模块可以根据不同实例动态地生成深层CTR模型的参数。广泛的实验评估结果表明，APG可以应用于各种深CTR模型，并显着提高其性能。同时，与常规的Deep CTR模型相比，APG可以将时间成本降低38.7 \％，并且内存使用率减少96.6 \％。我们已经在工业赞助的搜索系统中部署了APG，并分别获得了3 \％CTR增益和1 \％RPM增益。

Neuroevolution has greatly promoted Deep Neural Network (DNN) architecture design and its applications, while there is a lack of methods available across different DNN types concerning both their scale and performance. In this study, we propose a self-adaptive neuroevolution (SANE) approach to automatically construct various lightweight DNN architectures for different tasks. One of the key settings in SANE is the search space defined by cells and organs self-adapted to different DNN types. Based on this search space, a constructive evolution strategy with uniform evolution settings and operations is designed to grow DNN architectures gradually. SANE is able to self-adaptively adjust evolution exploration and exploitation to improve search efficiency. Moreover, a speciation scheme is developed to protect evolution from early convergence by restricting selection competition within species. To evaluate SANE, we carry out neuroevolution experiments to generate different DNN architectures including convolutional neural network, generative adversarial network and long short-term memory. The results illustrate that the obtained DNN architectures could have smaller scale with similar performance compared to existing DNN architectures. Our proposed SANE provides an efficient approach to self-adaptively search DNN architectures across different types.

We propose LiDAL, a novel active learning method for 3D LiDAR semantic segmentation by exploiting inter-frame uncertainty among LiDAR frames. Our core idea is that a well-trained model should generate robust results irrespective of viewpoints for scene scanning and thus the inconsistencies in model predictions across frames provide a very reliable measure of uncertainty for active sample selection. To implement this uncertainty measure, we introduce new inter-frame divergence and entropy formulations, which serve as the metrics for active selection. Moreover, we demonstrate additional performance gains by predicting and incorporating pseudo-labels, which are also selected using the proposed inter-frame uncertainty measure. Experimental results validate the effectiveness of LiDAL: we achieve 95% of the performance of fully supervised learning with less than 5% of annotations on the SemanticKITTI and nuScenes datasets, outperforming state-of-the-art active learning methods. Code release: https://github.com/hzykent/LiDAL.

Transformer models have achieved great success across many NLP problems. However, previous studies in automated ICD coding concluded that these models fail to outperform some of the earlier solutions such as CNN-based models. In this paper we challenge this conclusion. We present a simple and scalable method to process long text with the existing transformer models such as BERT. We show that this method significantly improves the previous results reported for transformer models in ICD coding, and is able to outperform one of the prominent CNN-based methods.

With the advanced request to employ a team of robots to perform a task collaboratively, the research community has become increasingly interested in collaborative simultaneous localization and mapping. Unfortunately, existing datasets are limited in the scale and variation of the collaborative trajectories, even though generalization between inter-trajectories among different agents is crucial to the overall viability of collaborative tasks. To help align the research community's contributions with realistic multiagent ordinated SLAM problems, we propose S3E, a large-scale multimodal dataset captured by a fleet of unmanned ground vehicles along four designed collaborative trajectory paradigms. S3E consists of 7 outdoor and 5 indoor sequences that each exceed 200 seconds, consisting of well temporal synchronized and spatial calibrated high-frequency IMU, high-quality stereo camera, and 360 degree LiDAR data. Crucially, our effort exceeds previous attempts regarding dataset size, scene variability, and complexity. It has 4x as much average recording time as the pioneering EuRoC dataset. We also provide careful dataset analysis as well as baselines for collaborative SLAM and single counterparts. Data and more up-to-date details are found at https://github.com/PengYu-Team/S3E.

6G时代的语义沟通被认为是一个有希望的沟通范式，可以突破传统通信的瓶颈。但是，其在多用户方案中的应用程序，尤其是广播案例，仍未探索。为了有效利用语义沟通启用的好处，在本文中，我们提出了一个一对一的语义通信系统。具体而言，我们建议使用一个启用的深神经网络（DNN），称为MR \ _DeepSc。通过为不同用户的语义功能利用语义功能，基于预训练的模型即Distilbert的语义识别器是为了区分不同用户的。此外，采用转移学习来加快新接收器网络的培训。仿真结果表明，在不同的通道条件下，提出的MR \ _DeepSc可以比其他基准测试获得最佳性能，尤其是在低信噪比（SNR）方面。